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Clustering Evolving Networks

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Algorithm Engineering

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9220))

Abstract

Roughly speaking, clustering evolving networks aims at detecting structurally dense subgroups in networks that evolve over time. This implies that the subgroups we seek for also evolve, which results in many additional tasks compared to clustering static networks. We discuss these additional tasks and difficulties resulting thereof and present an overview on current approaches to solve these problems. We focus on clustering approaches in online information from previous time steps in order to incorporate temporal smoothness or to achieve low running time. Moreover, we describe a collection of real world networks and generators for synthetic data that are frequently used for evaluation.

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Notes

  1. 1.

    http://www.informatik.uni-trier.de/~ley/db/.

  2. 2.

    Available at http://www.cs.cmu.edu/~enron/.

  3. 3.

    For further details and for downloading the whole dataset, please visit http://i11www.iti.uni-karlsruhe.de/en/projects/spp1307/emaildata.

  4. 4.

    http://realitycommons.media.mit.edu/realitymining.html.

  5. 5.

    http://www.flickr.com/.

  6. 6.

    https://dev.twitter.com/docs/streaming-apis#sampling.

  7. 7.

    http://www.livejournal.com/.

  8. 8.

    http://www.informatik.uni-trier.de/~ley/db/.

  9. 9.

    http://arxiv.org/.

  10. 10.

    http://i11www.iti.uni-karlsruhe.de/en/projects/spp1307/dyneval.

  11. 11.

    http://www.cs.cornell.edu/projects/kddcup/datasets.html.

  12. 12.

    http://snap.stanford.edu/data/index.html.

  13. 13.

    http://www.projecthoneypot.org/.

  14. 14.

    https://delicious.com/.

  15. 15.

    http://netsg.cs.sfu.ca/youtubedata/.

  16. 16.

    http://snap.stanford.edu/data/.

  17. 17.

    http://www-personal.umich.edu/~mejn/netdata/.

  18. 18.

    http://deim.urv.cat/~aarenas/data/welcome.htm.

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    Tanja Hartmann, Andrea Kappes & Dorothea Wagner

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Hartmann, T., Kappes, A., Wagner, D. (2016). Clustering Evolving Networks. In: Kliemann, L., Sanders, P. (eds) Algorithm Engineering. Lecture Notes in Computer Science(), vol 9220. Springer, Cham. https://doi.org/10.1007/978-3-319-49487-6_9

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